Deep Reinforcement Learning for Efficient Neural Architecture Search (ENAS) in PyTorch, i.e., AutoML. Code based on the paper https://arxiv.org/abs/1802.03268 and repo https://github.com/RualPerez/AutoML
How to run the pipeline:
-
Clone the whole repository
git clone https://github.com/Gemini321/AutoML.git -
Create virtualenv
virtualenv -p /usr/bin/python3.7 virtualenv/AutoML -
Activate virtualenv
source virtualenv/AutoML/bin/activate -
Install libraries
pip3 install -r requirements.txt -
Run the main script, for instance:
python3 main.py --num_episodes 5 --batch 5
Note that you can get a help of how to run the main script by:
python3 main.py -h
Once the whole steps have run successfully, the next times you only need to run the last step 5.
Output: The main script saves the trained policy/controller net as policy.pt
| File / Folder | Description |
|---|---|
| main.py | Main script with runs the AutoML experiment |
| *.py | An auxiliar script necessary to run main.py, its detailed description has been written as python documentation |
| Policy_Gradient_AutoML.ipynb | Jupyter Notebook designed to help users to understand how this project has been developed |
| article.pdf | Basic article that describes the principles of this project (theory-related). Here it can be found the results. |
| requirements.txt | Version of the python libraries necessary to run the main script |
| images/ | Images used for the article |